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WT24/25 Fundamentals of Business Analytics

39720 Fundamentals of Business Analytics

Tailor-made E-/Online course on "Data Literacy" in the master's degree

Fundamentals of Business Analytics

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Language

English

Location

E-learning/online course

Course Period

02.10.2024- 11.12.2024

ECTS

5 ECTS

SWS

Exam1: 27.11.2024 ,16:00 - 17:25 , Location WIWI CR30/CR31/32.

 Registration period: 02.10.-10.11.2024, deregistration deadline: 17.11.2024

Registration Form

Exam 2: 11.12.2024 , 16.00-17.25, location:WIWI CR30/CR31/32.

Registration period: 27.11.-01.12.2024 ,deregistration deadline: 06.12.2024

 

Exam Review Date :  -  Registration period: -   Office:   

2 SWS (150h mainly own work, possibly some hours of attendance)

Calculation basis: 15 weeks in a semester, including an examination week; each SWS corresponds to 60 minutes.

Semester 1
Background Basic knowledge in quantitative methods at the level of a management-oriented or economics-oriented bachelor’s degree

According to § 3 of the study and examination regulations for the Master's program in Business Administration

Against the background of continuous advances in digital technologies, competencies in Data Analytics and Data-Driven Decision Making, summarized as Data Literacy and the fundamentals of Mathematics and Statistics (Mathematical Literacy) form a fundamental framework of modern management. This innovative online course allows its participants to refresh and strengthen these competencies employing an highly individual learning scheme.

The course covers four subject areas.:

1) Fundamentals of Mathematics:

  • Sums, products, sets, linear equations, inequalities
  • Calculus (functions, limits, derivatives and integration)
  • Linear algebra (matrix algebra and systems of linear equations)

2) Fundamentals of Statistics

  • Random variables and stochastic modeling
  • Estimation and test theory
  • Regression modeling

3) Fundamentals of Management Science

  • Modeling of optimization problems
  • Introduction to algorithms, heuristics and metaheuristics
  • Linear programming

4) Fundamentals of Empirical Research Methods

  • Business research process
  • Primary and secondary data collection methods
  • Hypothesis testing

  • The main objective of this course is to build up and strengthen the core competencies of mathematical literacy and data literacy
  • The aim of the course is to equip students with a solid mathematical and analytical knowledge to successfully study questions and address challenges in the field of modern digital Management, like Data Analytics or Data-Driven Decision Making.
  • The course prepares students for advanced courses in the field of data literacy offered in the Master’s program

  • E-learning/online course (possibly accompanied by some supporting teaching sessions)
  • Intensive block course (~8 weeks) with individual learning organization
  • Participants take a mandatory online placement test at the beginning of the course to assess their current knowledge and competencies.
  • Based on the test results, participants will get their individual learning objectives for the course.
  • After the test, participants get access to a structured, comprehensive e-library with teaching videos, quizzes, tests, forums, scripts and other online materials.
  • Participants learn with the provided material in a flexible manner and according to their individual needs to achieve the qualification objectives.
  • Optional weekly consultation hours (online, Zoom) for organizational questions
  • Optional webinars (Zoom) for revision and questions on learning material

  • Portfolio examination. The final grade depends on the successful completion of e-assessments qualifying in all four subject areas of the course. 

Key textbooks

  • Alwan, L. C., Craig, B. A., and McCabe G. P. (2020). The Practice of Statistics for Business and Economics. 5th edition. Macmillan International Higher Education: New York.
  • Bertsimas, D., and Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific: Massachussets.
  • Gillard, J. (2020) A First Course in Statistical Inference. Springer: Cham.
  • Luderer, B., Nollau, V., and Vetters, K. (2007). Mathematical Formulas for Economists. 3rd edition. Springer: Berlin, Germany.
  • Navarro, D. (2018) Learning Statistics with R, open.umn.edu/opentextbooks/textbooks/559
  • Quinlan, C., Babin, B., Carr, J. Griffin, M., and Zikmund, W. G. (2019). Business Research Methods. 2nd edition. South-Western, Cengage Learning: Andover, UK.
  • Simon, C.P., and Blume, L. (1994). Mathematics for Economists. WW Norton & Co: London, UK.
  • Winston, W. (2003). Operations Research: Applications and Algorithms. Brooks/Cole: Belmont
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